Challenges and Future Directions of Big Data and Artificial Intelligence in Education
We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge edu...
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doaj-1e49c1f9ef714602916eadabeb20273e2020-11-25T03:41:36ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-10-011110.3389/fpsyg.2020.580820580820Challenges and Future Directions of Big Data and Artificial Intelligence in EducationHui Luan0Peter Geczy1Hollis Lai2Janice Gobert3Janice Gobert4Stephen J. H. Yang5Hiroaki Ogata6Jacky Baltes7Rodrigo Guerra8Ping Li9Chin-Chung Tsai10Chin-Chung Tsai11Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, TaiwanNational Institute of Advanced Industrial Science and Technology, Tsukuba, JapanSchool of Dentistry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, CanadaGraduate School of Education, Rutgers – The State University of New Jersey, New Brunswick, NJ, United StatesApprendis, LLC, Berlin, MA, United StatesDepartment of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Central University, Taoyuan City, TaiwanGraduate School of Informatics, Kyoto University, Kyoto, JapanDepartment of Electrical Engineering, College of Technology and Engineering, National Taiwan Normal University, Taipei, TaiwanCentro de Tecnologia, Universidade Federal de Santa Maria, Santa Maria, Brazil0Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Kowloon, Hong KongInstitute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, Taiwan1Program of Learning Sciences, National Taiwan Normal University, Taipei, TaiwanWe discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of “cold” technology and “warm” humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.https://www.frontiersin.org/articles/10.3389/fpsyg.2020.580820/fullbig dataartificial intelligenceeducationlearningteaching |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hui Luan Peter Geczy Hollis Lai Janice Gobert Janice Gobert Stephen J. H. Yang Hiroaki Ogata Jacky Baltes Rodrigo Guerra Ping Li Chin-Chung Tsai Chin-Chung Tsai |
spellingShingle |
Hui Luan Peter Geczy Hollis Lai Janice Gobert Janice Gobert Stephen J. H. Yang Hiroaki Ogata Jacky Baltes Rodrigo Guerra Ping Li Chin-Chung Tsai Chin-Chung Tsai Challenges and Future Directions of Big Data and Artificial Intelligence in Education Frontiers in Psychology big data artificial intelligence education learning teaching |
author_facet |
Hui Luan Peter Geczy Hollis Lai Janice Gobert Janice Gobert Stephen J. H. Yang Hiroaki Ogata Jacky Baltes Rodrigo Guerra Ping Li Chin-Chung Tsai Chin-Chung Tsai |
author_sort |
Hui Luan |
title |
Challenges and Future Directions of Big Data and Artificial Intelligence in Education |
title_short |
Challenges and Future Directions of Big Data and Artificial Intelligence in Education |
title_full |
Challenges and Future Directions of Big Data and Artificial Intelligence in Education |
title_fullStr |
Challenges and Future Directions of Big Data and Artificial Intelligence in Education |
title_full_unstemmed |
Challenges and Future Directions of Big Data and Artificial Intelligence in Education |
title_sort |
challenges and future directions of big data and artificial intelligence in education |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Psychology |
issn |
1664-1078 |
publishDate |
2020-10-01 |
description |
We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of “cold” technology and “warm” humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning. |
topic |
big data artificial intelligence education learning teaching |
url |
https://www.frontiersin.org/articles/10.3389/fpsyg.2020.580820/full |
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